Lu, Hao, Cao, N., Hu, X. and Chen, Yunfei (2013) Moving target imaging for MIMO radar using CS-SLIM. Journal of Information and Computational Science, Volume 10 (Number 10). pp. 2971-2980. doi:10.12733/jics20101882

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Abstract

Multiple-input Multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. Many methods were proposed to provide accurate MIMO angle-range-Doppler images of moving targets. Sparse Learning via Iterative Minimization (SLIM) can provide more accurate estimates in that case. However, when using the Bayesian Information Criterion (BIC) to determine the user parameter automatically for a less sparse scene, SLIM has high complexity. In this paper, we propose an improved approach as Sparse Learning via Iterative Minimization based on Compressive Sampling (CS-SLIM) for targets imaging. CS-SLIM can provide targets imaging as almost accurate as but with a lower computational burden than SLIM. The measurement matrix and the computational complexity for CS-SLIM are discussed. Mean Squared Errors (MSEs) at various measurement samples are provided to demonstrate the performance of the proposed approach in sparse scenes

Item Type:	Journal Article
Divisions:	Faculty of Science > Engineering
Journal or Publication Title:	Journal of Information and Computational Science
Publisher:	Binary Information Press
ISSN:	1548-7741
Official Date:	2013
Dates:	Date Event 2013 Published
Volume:	Volume 10
Number:	Number 10
Page Range:	pp. 2971-2980
DOI:	10.12733/jics20101882
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Related URLs:	Publisher

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